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Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations

Thomas Pa Debray1,2, Gabrielle Simoneau3, Massimiliano Copetti4

  • 1Julius Centrum voor Gezondheidswetenschappen en Eerstelijns Geneeskunde, Utrecht, Netherlands.

Statistical Methods in Medical Research
|June 12, 2023
PubMed
Summary

Multilevel multiple imputation accurately analyzes real-world data collected at irregular times. This method improves treatment effect estimates and confidence interval coverage for disease-modifying therapies in multiple sclerosis.

Keywords:
Clustered datacomparative effectivenessconfirmed disability progressionlongitudinal datamultiple imputationmultiple sclerosisreal-world data

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Area of Science:

  • Biostatistics
  • Real-world data analysis
  • Longitudinal data modeling

Background:

  • Real-world data (RWD) enables comparative treatment effectiveness research in clinical practice.
  • RWD often features selective outcome recording and irregular measurement times, complicating analysis.
  • Existing imputation methods may not capture longitudinal trajectories or handle missingness adequately.

Purpose of the Study:

  • To propose an extension of multilevel multiple imputation for analyzing RWD with irregular observation times.
  • To evaluate the performance of this novel imputation method in a multiple sclerosis (MS) case study.
  • To compare multilevel multiple imputation against standard single imputation techniques.

Main Methods:

  • Developed an extension of multilevel multiple imputation tailored for irregularly timed longitudinal outcomes.
  • Applied the method to analyze time to confirmed disability progression using Expanded Disability Status Scale (EDSS) data in MS patients.
  • Conducted a simulation study to assess bias and confidence interval coverage compared to single imputation.

Main Results:

  • Multilevel multiple imputation yielded less biased treatment effect estimates.
  • The proposed method demonstrated improved confidence interval coverage.
  • Effectiveness was observed even when outcomes were missing not at random (MNAR).

Conclusions:

  • Multilevel multiple imputation is a robust method for analyzing longitudinal RWD with irregular sampling.
  • This approach enhances the reliability of treatment comparisons in real-world settings.
  • The method offers a valuable tool for understanding disease trajectories and treatment effects in conditions like multiple sclerosis.